Towards a remote sensing data based evapotranspiration estimation in Northern Australia using a simple random forest approach

In this work we have developed a random forest regressor to predict daily evapotranspiration in three eddy-covariance sites in Northern Australia from in-situ meteorological data and fluxes, and satellite leaf area index and land surface temperature data. The variable analysis for the random forest regressor suggests that leaf area index is the most important one at this temporal scale. A development sample corresponding to the period 2010–2013 was used, while the year 2014 has been separated for testing. Using this approach, we have obtained satisfactory performances in the three sites, with RMSE errors around 1 mm/day (and relative RMSEs ~0.3) in comparison to the measured values. With the final aim of testing the predictive capability of a model that uses remote sensing data as input, regressors that predict evapotranspiration exclusively from leaf area index and land surface temperature, have been trained resulting in reasonable performances. The RMSEs over the test set are ~1.1−1.2 mm/day. In all cases, the errors are comparable to those obtained in previous works that use similar locations and different methods. When compared to the measured values, the random forest predictions of evapotranspiration are more accurate than the global MODIS ET 8-day 1 km product, which was used as benchmark for the performance evaluation of our models, in the three selected locations. •Random Forest regressors were used to estimate daily evapotranspiration in Australia.•Satellite LAI and LST models were tested using in-situ data and the MODIS ET product.•Reasonable performances were obtained in three eddy-covariance sites over the NATT.